{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "AKHCRNet Updated v1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuAn-j2fl6_Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, ZeroPadding2D\n",
        "from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Concatenate, Lambda\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import regularizers, activations\n",
        "import os\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_curve, auc\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IqK_bXJrmOZl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "1da3d6db-f55a-42f1-92ce-72c464f0a233"
      },
      "source": [
        "import os\n",
        "os.environ['KAGGLE_USERNAME'] = \"theroyakash\"\n",
        "os.environ['KAGGLE_KEY'] = \"SECRET_KEY\"\n",
        "!kaggle datasets download -d theroyakash/_SECRETKEY_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading akhcrnetv1dataprivate.zip to /content\n",
            "100% 186M/187M [00:07<00:00, 32.8MB/s]\n",
            "100% 187M/187M [00:07<00:00, 27.4MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U5UEz6pUmPBt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from zipfile import ZipFile\n",
        "\n",
        "with ZipFile('akhcrnetv1dataprivate.zip', 'r') as zipObj:\n",
        "   # Extract all the contents of zip file in current directory\n",
        "   zipObj.extractall()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZX7O6eVmSI9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras_preprocessing\n",
        "from keras_preprocessing import image\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "\n",
        "\n",
        "TRAINING_DIR = \"/content/Images\"\n",
        "batch_size = 256"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-oYilIEmUJo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2fe5b31f-f2ed-48d5-f580-0e843bcb4815"
      },
      "source": [
        "train_datagen = ImageDataGenerator(rescale=1./255,\n",
        "    # shear_range=0.2,\n",
        "    # zoom_range=0.2,\n",
        "    horizontal_flip=False,\n",
        "    validation_split=0.28) # set validation split\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    TRAINING_DIR,\n",
        "    target_size=(32,32),\n",
        "    color_mode='grayscale',\n",
        "\tclass_mode='categorical',\n",
        "    batch_size = batch_size,\n",
        "    subset='training') # set as training data\n",
        "\n",
        "validation_generator = train_datagen.flow_from_directory(\n",
        "    TRAINING_DIR, # same directory as training data\n",
        "    target_size=(32,32),\n",
        "    color_mode='grayscale',\n",
        "\tclass_mode='categorical',\n",
        "    batch_size = batch_size,\n",
        "    subset='validation') # set as validation data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 118394 images belonging to 84 classes.\n",
            "Found 45983 images belonging to 84 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xgTU2psbmFHE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AKHCRNetV1():\n",
        "    \n",
        "    def make_model(self):\n",
        "        \n",
        "        input_shape = self.input_shape\n",
        "        input_layer = Input(input_shape)\n",
        "        conv2D_1 = Conv2D(32, (5, 5), activation='relu', padding='same', name='conv2D_1')(input_layer)\n",
        "        conv2D_2 = Conv2D(32, (5, 5), padding='same', name='conv2D_2')(conv2D_1)\n",
        "        batchnorm_1 = BatchNormalization(name='first_batchNorm_layer')(conv2D_2)\n",
        "        activation1 = Activation('relu')(batchnorm_1)\n",
        "        \n",
        "        maxpool_1 = MaxPooling2D(pool_size=(2,2))(batchnorm_1)\n",
        "        \n",
        "        first_branch_0_conv2D_1 = Conv2D(128, (1, 1), activation='relu', padding='same', name='first_branch_0_conv2D_1')(maxpool_1)\n",
        "        first_branch_0_conv2D_2 = Conv2D(128, (5, 5), activation='relu', padding='same', name='first_branch_0_conv2D_2')(first_branch_0_conv2D_1)\n",
        "        \n",
        "        first_branch_1_conv2D_1 = Conv2D(128, (1, 1),activation='relu',padding='same', name='first_branch_1_conv2D_1')(maxpool_1)\n",
        "        first_branch_1_conv2D_2 = Conv2D(128, (3, 3),activation='relu',padding='same', name='first_branch_1_conv2D_2')(first_branch_1_conv2D_1)\n",
        "        \n",
        "        first_branch_2_conv2D = Conv2D(128, (1, 1),activation='relu',padding='same', name='first_branch_2_conv2D')(maxpool_1)\n",
        "        \n",
        "        first_branch_3_MaxPool_1 = MaxPooling2D((3,3), strides=(1,1), padding='same', name='first_branch_3_MaxPool_1')(maxpool_1)\n",
        "        first_branch_3_Convolution = Conv2D(64, (1,1), padding='same', activation='relu', name='first_branch_3_Convolution')(first_branch_3_MaxPool_1)\n",
        "                \n",
        "        concatened_first_branch = Concatenate()([first_branch_0_conv2D_2, first_branch_1_conv2D_2, first_branch_2_conv2D, first_branch_3_Convolution])\n",
        "        concatenation_activation = Activation('relu')(concatened_first_branch)\n",
        "\n",
        "        conv2D_3 = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv2D_3')(concatenation_activation)\n",
        "        maxpool_2 = MaxPooling2D(pool_size=(2,2))(conv2D_3)\n",
        "        conv2D_4 = Conv2D(256, (3, 3), padding='same', name='conv2D_4')(maxpool_2)\n",
        "        batchnorm_2 = BatchNormalization(name='second_batchNorm_layer')(conv2D_4)\n",
        "        activation2 = Activation('relu')(batchnorm_2)\n",
        "\n",
        "        conv2D_5 = Conv2D(512, (3, 3), activation='relu', padding='same', name='conv2D_5')(activation2)\n",
        "        maxpool_3 = MaxPooling2D(pool_size=(2,2))(conv2D_5)\n",
        "        conv2D_6 = Conv2D(512, (3, 3), padding='same', name='conv2D_6')(maxpool_3)\n",
        "        batchnorm_3 = BatchNormalization(name='third_batchNorm_layer')(conv2D_6)\n",
        "        activation3 = Activation('relu')(batchnorm_3)\n",
        "        maxpool_4 = MaxPooling2D(pool_size=(2,2))(activation3)\n",
        "        \n",
        "        flattened_before_dense = Flatten()(maxpool_4)\n",
        "        dense1 = Dense(1024, activation='relu', name='firstDenseLayer', kernel_regularizer= keras.regularizers.l2(0.001))(flattened_before_dense)\n",
        "        dense2 = Dense(512, activation='relu', name='SecondDenseLayer', kernel_regularizer= keras.regularizers.l2(0.001))(dense1)\n",
        "        dropout1 = Dropout(0.5, name='FirstDropOutLayer')(dense2)\n",
        "        dense3 = Dense(256, activation='relu', name='ThirdDenseLayer', kernel_regularizer= keras.regularizers.l2(0.001))(dropout1)\n",
        "        \n",
        "        dense4 = Dense(128, activation='relu', name='FourthDenseLayer')(dense3)\n",
        "\n",
        "        prediction_branch = Dense(self.output,activation='softmax', name='FinalSoftmaxLayer')(dense4)\n",
        "\n",
        "        model = Model(inputs=input_layer, outputs=prediction_branch)\n",
        "        \n",
        "        \n",
        "        return model\n",
        "    \n",
        "    def show_graph(self):\n",
        "        return model.summary()\n",
        "    \n",
        "    def __init__(self, input_shape, output):\n",
        "        self.input_shape = input_shape\n",
        "        self.output = output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYZEIQ2fmHdE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "architecture = AKHCRNetV1(input_shape= (32, 32, 1), output=84)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ppRxPVqtmJnb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = architecture.make_model()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKlXpSPBmLDX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "architecture.show_graph()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23nXAsk3mZVr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "learning_rate = 0.001\n",
        "epochs = 20\n",
        "batch_size = 256"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fP58YpOza7P0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "cc79371c-2bc4-48de-a8fe-6196fbc4ee71"
      },
      "source": [
        "model.compile(loss='categorical_crossentropy', \n",
        "                metrics=['accuracy'],\n",
        "                optimizer=Adam(learning_rate=0.001))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 32, 32, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2D_1 (Conv2D)               (None, 32, 32, 32)   832         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2D_2 (Conv2D)               (None, 32, 32, 32)   25632       conv2D_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "first_batchNorm_layer (BatchNor (None, 32, 32, 32)   128         conv2D_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 16, 16, 32)   0           first_batchNorm_layer[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "first_branch_0_conv2D_1 (Conv2D (None, 16, 16, 128)  4224        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "first_branch_1_conv2D_1 (Conv2D (None, 16, 16, 128)  4224        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "first_branch_3_MaxPool_1 (MaxPo (None, 16, 16, 32)   0           max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "first_branch_0_conv2D_2 (Conv2D (None, 16, 16, 128)  409728      first_branch_0_conv2D_1[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "first_branch_1_conv2D_2 (Conv2D (None, 16, 16, 128)  147584      first_branch_1_conv2D_1[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "first_branch_2_conv2D (Conv2D)  (None, 16, 16, 128)  4224        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "first_branch_3_Convolution (Con (None, 16, 16, 64)   2112        first_branch_3_MaxPool_1[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 16, 16, 448)  0           first_branch_0_conv2D_2[0][0]    \n",
            "                                                                 first_branch_1_conv2D_2[0][0]    \n",
            "                                                                 first_branch_2_conv2D[0][0]      \n",
            "                                                                 first_branch_3_Convolution[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 16, 16, 448)  0           concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2D_3 (Conv2D)               (None, 16, 16, 256)  1032448     activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 8, 8, 256)    0           conv2D_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2D_4 (Conv2D)               (None, 8, 8, 256)    590080      max_pooling2d_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "second_batchNorm_layer (BatchNo (None, 8, 8, 256)    1024        conv2D_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 8, 8, 256)    0           second_batchNorm_layer[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "conv2D_5 (Conv2D)               (None, 8, 8, 512)    1180160     activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 4, 4, 512)    0           conv2D_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2D_6 (Conv2D)               (None, 4, 4, 512)    2359808     max_pooling2d_2[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "third_batchNorm_layer (BatchNor (None, 4, 4, 512)    2048        conv2D_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 4, 4, 512)    0           third_batchNorm_layer[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 2, 2, 512)    0           activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 2048)         0           max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "firstDenseLayer (Dense)         (None, 1024)         2098176     flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "SecondDenseLayer (Dense)        (None, 512)          524800      firstDenseLayer[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "FirstDropOutLayer (Dropout)     (None, 512)          0           SecondDenseLayer[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "ThirdDenseLayer (Dense)         (None, 256)          131328      FirstDropOutLayer[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "FourthDenseLayer (Dense)        (None, 128)          32896       ThirdDenseLayer[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "FinalSoftmaxLayer (Dense)       (None, 84)           10836       FourthDenseLayer[0][0]           \n",
            "==================================================================================================\n",
            "Total params: 8,562,292\n",
            "Trainable params: 8,560,692\n",
            "Non-trainable params: 1,600\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M75tEv72yWIN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "38431806-1591-4533-8182-458958549c0f"
      },
      "source": [
        "len(model.layers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "talRJsFhvfHZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "ea5c7c7e-a5e3-44c8-e362-92ec319ca907"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(model, 'AKHCRNetV2.png', show_shapes=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1sviqijjfiGm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "filepath = \"model.h5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
        "callbacks_list = [checkpoint]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9jRevTgLmb00",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "2d47e8fc-ad4b-4b6d-84e6-53537692a213"
      },
      "source": [
        "history = model.fit(train_generator,\n",
        "                    epochs=5,\n",
        "                    steps_per_epoch = train_generator.samples // batch_size,\n",
        "                    validation_data = validation_generator, \n",
        "                    validation_steps = validation_generator.samples // batch_size, callbacks=callbacks_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "462/462 [==============================] - ETA: 0s - loss: 2.8836 - accuracy: 0.3881\n",
            "Epoch 00001: val_loss improved from inf to 1.36770, saving model to model.h5\n",
            "462/462 [==============================] - 133s 288ms/step - loss: 2.8836 - accuracy: 0.3881 - val_loss: 1.3677 - val_accuracy: 0.6384\n",
            "Epoch 2/5\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.8584 - accuracy: 0.8167\n",
            "Epoch 00002: val_loss improved from 1.36770 to 0.44341, saving model to model.h5\n",
            "462/462 [==============================] - 131s 283ms/step - loss: 0.8584 - accuracy: 0.8167 - val_loss: 0.4434 - val_accuracy: 0.9239\n",
            "Epoch 3/5\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.6211 - accuracy: 0.8734\n",
            "Epoch 00003: val_loss improved from 0.44341 to 0.35326, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.6211 - accuracy: 0.8734 - val_loss: 0.3533 - val_accuracy: 0.9434\n",
            "Epoch 4/5\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.5183 - accuracy: 0.8952\n",
            "Epoch 00004: val_loss improved from 0.35326 to 0.32288, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.5183 - accuracy: 0.8952 - val_loss: 0.3229 - val_accuracy: 0.9493\n",
            "Epoch 5/5\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.4560 - accuracy: 0.9089\n",
            "Epoch 00005: val_loss improved from 0.32288 to 0.30081, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.4560 - accuracy: 0.9089 - val_loss: 0.3008 - val_accuracy: 0.9511\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s43aB_u3UsJV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1c5c596d-cb2e-4cf2-845a-979b32852690"
      },
      "source": [
        "from keras import backend as K\n",
        "print(model.optimizer.learning_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'Adam/learning_rate:0' shape=() dtype=float32, numpy=0.001>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HLQ1T9qnoH7Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "9e48a486-7b1e-48f1-cb65-3190b56afc07"
      },
      "source": [
        "new_model = tf.keras.models.load_model(filepath)\n",
        "K.set_value(new_model.optimizer.learning_rate, 0.0001)\n",
        "\n",
        "history = new_model.fit(train_generator,\n",
        "                    epochs=3,\n",
        "                    steps_per_epoch = train_generator.samples // batch_size,\n",
        "                    validation_data = validation_generator,\n",
        "                    validation_steps = validation_generator.samples // batch_size, callbacks=callbacks_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.3121 - accuracy: 0.9467\n",
            "Epoch 00001: val_loss improved from 0.30081 to 0.24151, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.3121 - accuracy: 0.9467 - val_loss: 0.2415 - val_accuracy: 0.9661\n",
            "Epoch 2/3\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.2722 - accuracy: 0.9549\n",
            "Epoch 00002: val_loss improved from 0.24151 to 0.23421, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.2722 - accuracy: 0.9549 - val_loss: 0.2342 - val_accuracy: 0.9663\n",
            "Epoch 3/3\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.2456 - accuracy: 0.9592\n",
            "Epoch 00003: val_loss improved from 0.23421 to 0.22147, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.2456 - accuracy: 0.9592 - val_loss: 0.2215 - val_accuracy: 0.9679\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IlBu6Qan0hkz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "f1b42495-a407-4ec4-fb70-ff9a336eddc2"
      },
      "source": [
        "new_model2 = tf.keras.models.load_model(filepath)\n",
        "print(new_model2.optimizer.learning_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'learning_rate:0' shape=() dtype=float32, numpy=1e-04>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fkRd561b0wFT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "4a8f6bcb-157d-4252-ff52-4fa85543afe5"
      },
      "source": [
        "from keras import backend as K\n",
        "K.set_value(new_model2.optimizer.learning_rate, 0.00004)\n",
        "print(new_model2.optimizer.learning_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'learning_rate:0' shape=() dtype=float32, numpy=4e-05>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A42PEMAcvCgL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "b0ebe69e-c2d8-42f3-baef-9d8d95817026"
      },
      "source": [
        "history = new_model2.fit(train_generator,\n",
        "                    epochs=3,\n",
        "                    steps_per_epoch = train_generator.samples // batch_size,\n",
        "                    validation_data = validation_generator,\n",
        "                    validation_steps = validation_generator.samples // batch_size, callbacks=callbacks_list)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.2196 - accuracy: 0.9651\n",
            "Epoch 00001: val_loss improved from 0.22147 to 0.22001, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.2196 - accuracy: 0.9651 - val_loss: 0.2200 - val_accuracy: 0.9679\n",
            "Epoch 2/3\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.2105 - accuracy: 0.9668\n",
            "Epoch 00002: val_loss improved from 0.22001 to 0.21911, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.2105 - accuracy: 0.9668 - val_loss: 0.2191 - val_accuracy: 0.9677\n",
            "Epoch 3/3\n",
            "462/462 [==============================] - ETA: 0s - loss: 0.2016 - accuracy: 0.9687\n",
            "Epoch 00003: val_loss improved from 0.21911 to 0.21612, saving model to model.h5\n",
            "462/462 [==============================] - 131s 284ms/step - loss: 0.2016 - accuracy: 0.9687 - val_loss: 0.2161 - val_accuracy: 0.9680\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vXGoZuVVu2Yq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "4ad2c2f2-414b-4e49-9dbe-041e05c02d48"
      },
      "source": [
        "new_model3 = tf.keras.models.load_model(filepath)\n",
        "print(new_model3.optimizer.learning_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'learning_rate:0' shape=() dtype=float32, numpy=4e-05>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55ORZri87-4A",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "1ee5466b-5a6b-4528-8102-479496d117c5"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "adc.json\t\t   AKHCRNetV2.png  model.h5\n",
            "akhcrnetv1dataprivate.zip  Images\t   sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uj5XAblxme2y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download('AKHCRNetV2.png')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}
